A Fast Iterative Airborne STAP for Coprime Sampling Structure

In the space-time adaptive processing (STAP) algorithm, the coprime structure can achieve better clutter suppression performance at lower hardware costs compared to the uniform linear structure. However, in practical scenarios, the performance of the algorithm is limited by the number of training samples. Therefore, this paper proposes a fast iterative heterogeneous STAP algorithm. To effectively estimate the clutter covariance matrix (CCM) and ensure its low rank, truncated nuclear norm regularization technique is introduced, which converts the non-convex problem into a convex optimization problem. Then, the virtual CCM is solved through a two-step iterative algorithm. The effectiveness and accuracy of the proposed algorithm are verified through simulation experiments.